Oracle International Corporation

James Thomas McKendree

Egidio Loch Terra

David Anthony Madril

Richard Lee Krenek

Boonchanh Oupaxay

Susan Jane Beidler

Paz Centeno

Catherine H M Kuo

Gary Paul Allen

Rondy C Ng

Vivian Wei Wong

Alexander Y Rukhotskiy

Noone Savage

Noone Alma Savage Tongay

Casey Joe Frick

Luba Schuyler

Techniques for generating and maintaining a student academic ledger are disclosed. In some embodiments, student data is received from a first set of one or more members of a blockchain network. In response, one or more distributed ledgers are updated in the blockchain network. The distributed ledgers are accessible to a student member of the blockchain network using a private key. The blockchain network receives requests from the student member to initiate a transaction with a second set of one or more members that requires access to at least a subset of the student data. Responsive to the request, the second set of one or more members are granted access to at least the subset of the student data from at least one distributed ledger.

Distributed and blockchain-based student academic ledger systems and methods

Operations include aggregating, analyzing, and displaying information about prospects for an educational institution. The system may aggregate prospect information by traversing, for example, lower education databases, social networking websites, or testing organization databases. The system may evaluate prospects which are potential applicants to generate recommendations for contact by a recruiter. Alternatively, or additionally, the system may evaluate prospects which have applied for admission to the institution to generate recommendations for acceptance by an admission officer. The system may recommend a prospect based on the prospect's potential for success in the institution. Success may correspond to metrics such as a likelihood of graduating from the educational institution or a likelihood of obtaining employment subsequent to graduating from the institution. Prospects sharing attributes with previously successful students may be selected for recommendation. Prospects which do not share attributes with previously successful students may not be selected for recommendation.

Recruiting and admission system

Operations include recommending and presenting academic programs for students. An academic program recommendation model generates recommendations based on a target student's specific characteristics. An academic program is recommended to the target student if the target student's characteristics match the characteristics of other students that have successfully completed the academic program. An academic program is not recommended to the target student if the target student's characteristics match the characteristics of other students that have failed to complete the academic program after some threshold period of time. The system presents an interface for comparing multiple academic programs that have been evaluated in view of a target student's specific characteristics. The interface concurrently lists each of a set of academic programs with a corresponding likelihood of success determined for the target student.

Academic program recommendation

An event recommendation system recommends events for a candidate attendee. The system recommends an event based on characteristics of the candidate attendee and characteristics of prior attendees that attended a prior occurrence of the event. A prior attendee may have a positive or negative experience with the prior occurrence of the event. A positive experience may be defined, for example, as enjoying the event, completing the event, or performing well in the event. A negative experience may be defined, for example, as not enjoying the event, not completing the event, or not performing well in the event. An event is recommended to a candidate attendee if the candidate attendee has similar characteristics to a prior attendee that had a positive experience. An event is not recommended to a candidate attendee if the candidate attendee has similar characteristics as a prior attendee that had a negative experience.

Event recommendation system

Operations include identifying students at risk of dropping out or transferring from an academic institution. A student risk analyzer determines a risk score for a student, which may be used to determine whether the student is at-risk. The risk score may be based on a combination of factors. As an example, the risk score may be based on a similarity between (a) attributes of the student and (b) attributes of students that have previously dropped out or transferred to other institutions. A student retention interface displays risk information for students in aggregate form or individual form. The interface may display student information in a student-population view comprising aggregate student information. The interface may display a risk-category view comprising a subset of students. The interface may display an individual-profile view comprising detailed information about a particular student. The interface may allow for drill-down navigation between the views.

Student retention system

An event management system generates and displays an event schedule for an attendee. An initial order of the event schedule is based on (a) events already completed by the attendee, (b) events to be completed by the attendee to meet an event completion target, and (c) a set of ordering rules associated with the set of events to be completed by the attendee. The event management system may include an interface for modifying the event schedule in accordance with the set of ordering rules. The interface may include an initial set of events, in an initial order with respect to a timeline. The event management system may receive user input including a proposed modification to the initial order of the events. The event management system may or may not permit the proposed modification based on the set of ordering rules.

Event management system

In an embodiment, a first level of confidence corresponding to a first data set presented in a pre-populated form is computed. A visualization suggesting a need for user review in association with the first data set is displayed when the first level of confidence is determined to not meet a first threshold value. In addition, a second level of confidence corresponding to a second data set presented in the pre-populated form is computed. No visualization is presented when the second level of confidence is determined to meet the second threshold value. In an embodiment, an impact associated with an academic course modification is determined and identified according to a user selection from a displayed set of scheduled courses. The impact may include a financial impact, a graduation timeline impact, or a workload impact associated with the modification.

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Elizabeth, CO, United States of America

James Thomas McKendree